AI and Digital Health Set to Reshape Cardiometabolic Medicine, Major Review Finds

Cardiometabolic conditions—including cardiovascular disease, type 2 diabetes, chronic kidney disease, and metabolic dysfunction-associated steatotic liver disease, known as MASLD—remain among the leading causes of death and disability worldwide. A comprehensive review published in BMC Medicine argues that the tools now emerging from artificial intelligence and digital health research could fundamentally change how these diseases are predicted, prevented, and managed, but only if the field confronts a set of persistent technical and ethical obstacles. The review, led by Kelvin Ing Leong Tiong, Nur Elliyana Abd Rahman, and Lee-Ling Lim of Universiti Malaya, together with collaborators across South Korea, Hong Kong, and Australia, synthesises evidence from recent systematic reviews, meta-analyses, randomised clinical trials, and observational studies to map the current landscape of AI-driven cardiometabolic care.

The central argument of the review is that digital health technologies have demonstrated strong clinical potential yet have not fully transcended traditional intervention paradigms. Mobile applications, wearable sensors, telemedicine platforms, and machine learning models have each shown benefits in isolated settings, but the authors contend that the real transformation will come from integrating these components into coherent systems that connect patients, clinicians, and health records. The rapid digitalisation of healthcare, accelerated in recent years, has created the infrastructure for such integration, and the review evaluates whether the clinical evidence has kept pace with the technological enthusiasm.

One of the most striking findings concerns risk prediction. For decades, cardiometabolic risk has been estimated using tools such as the Framingham Risk Score, SCORE, QRISK3, and the more recent PREVENT equations, which combine a handful of variables—age, sex, blood pressure, cholesterol, smoking status, and diabetes—into a statistical estimate. Machine learning models, by contrast, can extract high-dimensional patterns from multimodal data streams, including electronic medical records, electrocardiograms, retinal images, genomic profiles, and continuous physiological measurements from wearables. The review highlights evidence that AI approaches can outperform these traditional risk models, particularly when they incorporate data types that classical equations cannot use, such as polygenic risk scores derived from single nucleotide polymorphisms, or subtle features extracted from routine electrocardiograms that correlate with future atrial fibrillation, left ventricular dysfunction, and other silent cardiac conditions.

Technical performance in these studies is typically quantified using metrics such as the area under the receiver operating characteristic curve, or AUROC, and the area under the precision-recall curve, alongside calibration measures like the Brier score. The review notes that models reporting strong discrimination in retrospective datasets often degrade when deployed in real-world clinical environments, a phenomenon driven by differences in patient demographics, data collection practices, and measurement standards between development and deployment settings. This gap between benchmark performance and clinical utility is a recurring theme, and the authors identify rigorous, real-world validation as the first of three priorities for future investment.

Beyond prediction, the review examines how artificial intelligence is being embedded into clinical decision support systems through integration with electronic medical records. When a model can read a patient’s longitudinal record—laboratory trends such as estimated glomerular filtration rate, alanine aminotransferase, and glycated haemoglobin, alongside medication histories and clinical notes processed by natural language processing—it can generate individualised recommendations at the point of care. In nephrology, for example, AI-assisted systems have been explored for predicting kidney function decline and optimising the timing of erythropoietin stimulating agent therapy. In hepatology, machine learning models incorporating genetic variants such as PNPLA3, TM6SF2, and APOL1 have been used to stratify liver disease risk in patients with obesity and diabetes. In diabetes research, unsupervised clustering of clinical and genetic data has reproduced and refined the now widely cited subtyping of the disease into five clusters, ranging from severe autoimmune diabetes to mild age-related diabetes, each with distinct trajectories and treatment responses.

The review also documents the contribution of consumer-facing digital health tools. Photoplethysmography signals from smartwatches and fitness bands, transdermal optical imaging, and smartphone-based screening have enabled continuous, passive monitoring of heart rhythm, blood pressure trends, and physical activity. Randomised trials of app-based coaching, remote monitoring, and technology-assisted hybrid cardiac rehabilitation show improvements in patient engagement, medication adherence, and selected clinical outcomes. Telemedicine has expanded access to specialist care for patients in rural and underserved regions, a benefit that became particularly visible during recent global health disruptions. These tools successfully promote engagement, improve outcomes, and enhance access—the three achievements the review credits to the current generation of digital health innovations.

Yet the same technologies that promise to democratise care risk deepening existing inequities. The review identifies inequality in digital health access as a significant barrier to sustained implementation. Wearables and smartphones are less prevalent among older adults, low-income populations, and communities in low- and middle-income countries, precisely the groups that carry a disproportionate burden of cardiometabolic disease. Algorithmic bias compounds this problem: models trained on datasets dominated by particular ethnic or socioeconomic groups may perform poorly when applied to others, generating inaccurate risk estimates and potentially misdirecting care. Generalisability across diverse demographics is therefore not a peripheral concern but a central test of whether these systems can serve whole populations rather than privileged subsets.

Data privacy and governance present a second cluster of challenges. Health data flowing through consumer devices and cloud platforms falls under a patchwork of regulations, including the Health Insurance Portability and Accountability Act in the United States, the General Data Protection Regulation in Europe, and the Asia Pacific Economic Cooperation Privacy Framework. The review argues that robust regulatory frameworks are essential to ensure that AI tools are safe, effective, and accountable before they influence clinical decisions. A third priority is the adoption of explainable artificial intelligence, or XAI. Deep learning models often operate as opaque systems whose internal reasoning cannot be inspected by clinicians, which undermines trust and complicates error analysis. Explainable approaches that surface the features driving a prediction—a specific electrocardiographic pattern, a laboratory trend, a genetic variant—allow clinicians to verify that a model is reasoning sensibly and to identify spurious associations before they cause harm.

The authors also situate these technologies within the broader shift toward precision medicine in cardiometabolic health. Traditional categories such as metabolically healthy obesity, metabolically unhealthy normal weight, and the homoeostasis model assessment of insulin resistance are being re-examined through the lens of high-dimensional data, allowing finer stratification of patients who superficially resemble one another but follow very different disease courses. Combining polygenic risk scores with clinical and behavioural data streams could, in principle, allow prevention strategies to be tailored to an individual’s specific risk architecture rather than applied uniformly. The review suggests that this convergence of genomics, sensing technology, and machine learning is where the greatest transformative potential lies, both for prevention and for treatment.

Taken together, the review offers a measured but optimistic verdict. Artificial intelligence and digital health innovations have already demonstrated that they can promote patient engagement, improve clinical outcomes, and enhance healthcare access in cardiometabolic medicine. However, the authors conclude that realising their transformative potential will require deliberate investment in three areas: rigorous validation in real-world settings rather than curated datasets, the adoption of explainable AI to make algorithmic reasoning transparent to clinicians and patients, and robust regulatory frameworks that safeguard privacy and equity while allowing innovation to proceed. If those conditions are met, the tools described in this synthesis could move from promising prototypes to standard components of cardiometabolic prevention and care, reshaping a field that currently accounts for an enormous share of global morbidity and mortality.

Subject of Research: Artificial intelligence and digital health innovations for cardiometabolic disease prevention and management

Article Title: Reimagining cardiometabolic health through artificial intelligence and digital health innovations

Article References: Tiong, K. I. L., Abd Rahman, N. E., Lee, Y.-H., Chan, W.-K., Lim, S.-K., Boey, V. W.-F., Lui, J. N., Tae, S.-K., & Lim, L.-L. (2026). Reimagining cardiometabolic health through artificial intelligence and digital health innovations. BMC Medicine. https://doi.org/10.1186/s12916-026-05196-x

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05196-x

Keywords: artificial intelligence, digital health, cardiometabolic disease, cardiovascular disease, diabetes, chronic kidney disease, MASLD, machine learning, clinical decision support, explainable AI, algorithmic bias, risk prediction